Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations18154
Missing cells2758
Missing cells (%)0.7%
Total size in memory3.0 MiB
Average record size in memory176.0 B

Variable types

Text12
Numeric10

Alerts

merch_zipcode has 2758 (15.2%) missing values Missing
amt is highly skewed (γ1 = 23.68201282) Skewed
trans_num has unique values Unique
is_fraud has 18049 (99.4%) zeros Zeros

Reproduction

Analysis started2025-06-28 20:31:58.857289
Analysis finished2025-06-28 20:31:59.245680
Duration0.39 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct693
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:06.919365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.10399912
Min length13

Characters and Unicode

Total characters419430
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Labadie LLC
2nd rowfraud_Lind-Buckridge
3rd rowfraud_Strosin-Cruickshank
4th rowfraud_Olson, Becker and Koch
5th rowfraud_Cormier, Stracke and Thiel
ValueCountFrequency (%)
and 6609
 
15.6%
llc 1390
 
3.3%
inc 1231
 
2.9%
sons 1014
 
2.4%
ltd 983
 
2.3%
plc 943
 
2.2%
group 719
 
1.7%
fraud_streich 136
 
0.3%
dietrich 130
 
0.3%
fraud_kilback 127
 
0.3%
Other values (804) 28951
68.6%
2025-06-28T21:32:07.274114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 40734
 
9.7%
r 37672
 
9.0%
d 29941
 
7.1%
e 26262
 
6.3%
u 26035
 
6.2%
n 24601
 
5.9%
24079
 
5.7%
f 19501
 
4.6%
_ 18154
 
4.3%
o 15646
 
3.7%
Other values (45) 156805
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 419430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 40734
 
9.7%
r 37672
 
9.0%
d 29941
 
7.1%
e 26262
 
6.3%
u 26035
 
6.2%
n 24601
 
5.9%
24079
 
5.7%
f 19501
 
4.6%
_ 18154
 
4.3%
o 15646
 
3.7%
Other values (45) 156805
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 419430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 40734
 
9.7%
r 37672
 
9.0%
d 29941
 
7.1%
e 26262
 
6.3%
u 26035
 
6.2%
n 24601
 
5.9%
24079
 
5.7%
f 19501
 
4.6%
_ 18154
 
4.3%
o 15646
 
3.7%
Other values (45) 156805
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 419430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 40734
 
9.7%
r 37672
 
9.0%
d 29941
 
7.1%
e 26262
 
6.3%
u 26035
 
6.2%
n 24601
 
5.9%
24079
 
5.7%
f 19501
 
4.6%
_ 18154
 
4.3%
o 15646
 
3.7%
Other values (45) 156805
37.4%
Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:07.369688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length10.51696596
Min length4

Characters and Unicode

Total characters190925
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpersonal_care
2nd rowentertainment
3rd rowgrocery_pos
4th rowgas_transport
5th rowentertainment
ValueCountFrequency (%)
gas_transport 1776
9.8%
home 1775
9.8%
grocery_pos 1721
9.5%
shopping_pos 1606
8.8%
kids_pets 1574
8.7%
personal_care 1352
7.4%
shopping_net 1344
7.4%
entertainment 1330
7.3%
food_dining 1289
7.1%
health_fitness 1208
 
6.7%
Other values (4) 3179
17.5%
2025-06-28T21:32:07.519982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 19807
10.4%
e 18275
9.6%
o 17201
9.0%
n 16757
8.8%
p 15004
 
7.9%
t 14980
 
7.8%
_ 14504
 
7.6%
r 12867
 
6.7%
i 11627
 
6.1%
a 9339
 
4.9%
Other values (10) 40564
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 190925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 19807
10.4%
e 18275
9.6%
o 17201
9.0%
n 16757
8.8%
p 15004
 
7.9%
t 14980
 
7.8%
_ 14504
 
7.6%
r 12867
 
6.7%
i 11627
 
6.1%
a 9339
 
4.9%
Other values (10) 40564
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 190925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 19807
10.4%
e 18275
9.6%
o 17201
9.0%
n 16757
8.8%
p 15004
 
7.9%
t 14980
 
7.8%
_ 14504
 
7.6%
r 12867
 
6.7%
i 11627
 
6.1%
a 9339
 
4.9%
Other values (10) 40564
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 190925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 19807
10.4%
e 18275
9.6%
o 17201
9.0%
n 16757
8.8%
p 15004
 
7.9%
t 14980
 
7.8%
_ 14504
 
7.6%
r 12867
 
6.7%
i 11627
 
6.1%
a 9339
 
4.9%
Other values (10) 40564
21.2%

amt
Real number (ℝ)

Skewed 

Distinct9853
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.85690592
Minimum1
Maximum8221.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:07.575606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q19.78
median47.08
Q383.635
95-th percentile191.8575
Maximum8221.84
Range8220.84
Interquartile range (IQR)73.855

Descriptive statistics

Standard deviation151.3190591
Coefficient of variation (CV)2.166128847
Kurtosis1039.049387
Mean69.85690592
Median Absolute Deviation (MAD)37.16
Skewness23.68201282
Sum1268182.27
Variance22897.45764
MonotonicityNot monotonic
2025-06-28T21:32:07.636044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.54 18
 
0.1%
1.25 13
 
0.1%
1.29 12
 
0.1%
6.45 12
 
0.1%
3.73 12
 
0.1%
4.2 12
 
0.1%
1.27 12
 
0.1%
1.64 11
 
0.1%
1.1 11
 
0.1%
5.73 11
 
0.1%
Other values (9843) 18030
99.3%
ValueCountFrequency (%)
1 2
 
< 0.1%
1.01 9
< 0.1%
1.02 1
 
< 0.1%
1.03 8
< 0.1%
1.04 10
0.1%
ValueCountFrequency (%)
8221.84 1
< 0.1%
8217.23 1
< 0.1%
4292.86 1
< 0.1%
4169.42 1
< 0.1%
3373.11 1
< 0.1%

gender
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:07.672849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18154
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowF
5th rowF
ValueCountFrequency (%)
f 9835
54.2%
m 8319
45.8%
2025-06-28T21:32:07.749809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 9835
54.2%
M 8319
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 9835
54.2%
M 8319
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 9835
54.2%
M 8319
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 9835
54.2%
M 8319
45.8%

street
Text

Distinct923
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:07.948483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length29
Mean length22.20656605
Min length12

Characters and Unicode

Total characters403138
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st row25887 Martin Inlet
2nd row25713 Hamilton Grove Apt. 903
3rd row77663 Colleen Freeway
4th row2807 Parker Station Suite 080
5th row35737 Kirby Fall Suite 409
ValueCountFrequency (%)
apt 4554
 
6.3%
suite 4282
 
5.9%
island 327
 
0.5%
michael 269
 
0.4%
brooks 254
 
0.4%
david 248
 
0.3%
islands 235
 
0.3%
common 234
 
0.3%
smith 228
 
0.3%
station 226
 
0.3%
Other values (1850) 61277
84.9%
2025-06-28T21:32:08.279783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53980
 
13.4%
e 25120
 
6.2%
a 20221
 
5.0%
i 18017
 
4.5%
t 17378
 
4.3%
r 15413
 
3.8%
n 15004
 
3.7%
s 14455
 
3.6%
l 12468
 
3.1%
o 12339
 
3.1%
Other values (52) 198743
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 403138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
53980
 
13.4%
e 25120
 
6.2%
a 20221
 
5.0%
i 18017
 
4.5%
t 17378
 
4.3%
r 15413
 
3.8%
n 15004
 
3.7%
s 14455
 
3.6%
l 12468
 
3.1%
o 12339
 
3.1%
Other values (52) 198743
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 403138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
53980
 
13.4%
e 25120
 
6.2%
a 20221
 
5.0%
i 18017
 
4.5%
t 17378
 
4.3%
r 15413
 
3.8%
n 15004
 
3.7%
s 14455
 
3.6%
l 12468
 
3.1%
o 12339
 
3.1%
Other values (52) 198743
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 403138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
53980
 
13.4%
e 25120
 
6.2%
a 20221
 
5.0%
i 18017
 
4.5%
t 17378
 
4.3%
r 15413
 
3.8%
n 15004
 
3.7%
s 14455
 
3.6%
l 12468
 
3.1%
o 12339
 
3.1%
Other values (52) 198743
49.3%

city
Text

Distinct850
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:08.495445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.649058059
Min length3

Characters and Unicode

Total characters157015
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st rowGraniteville
2nd rowBeaverdam
3rd rowMoundsville
4th rowStanchfield
5th rowVanderbilt
ValueCountFrequency (%)
city 310
 
1.4%
west 293
 
1.3%
north 206
 
0.9%
falls 204
 
0.9%
saint 167
 
0.7%
mount 166
 
0.7%
new 143
 
0.6%
san 140
 
0.6%
lake 138
 
0.6%
hill 117
 
0.5%
Other values (877) 20703
91.7%
2025-06-28T21:32:08.930578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 15149
 
9.6%
a 13003
 
8.3%
n 11588
 
7.4%
o 11553
 
7.4%
l 10872
 
6.9%
r 10606
 
6.8%
i 9799
 
6.2%
t 8497
 
5.4%
s 6165
 
3.9%
4433
 
2.8%
Other values (42) 55350
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 157015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15149
 
9.6%
a 13003
 
8.3%
n 11588
 
7.4%
o 11553
 
7.4%
l 10872
 
6.9%
r 10606
 
6.8%
i 9799
 
6.2%
t 8497
 
5.4%
s 6165
 
3.9%
4433
 
2.8%
Other values (42) 55350
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 157015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15149
 
9.6%
a 13003
 
8.3%
n 11588
 
7.4%
o 11553
 
7.4%
l 10872
 
6.9%
r 10606
 
6.8%
i 9799
 
6.2%
t 8497
 
5.4%
s 6165
 
3.9%
4433
 
2.8%
Other values (42) 55350
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 157015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15149
 
9.6%
a 13003
 
8.3%
n 11588
 
7.4%
o 11553
 
7.4%
l 10872
 
6.9%
r 10606
 
6.8%
i 9799
 
6.2%
t 8497
 
5.4%
s 6165
 
3.9%
4433
 
2.8%
Other values (42) 55350
35.3%

state
Text

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:09.078163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters36308
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowVT
2nd rowOH
3rd rowWV
4th rowMN
5th rowPA
ValueCountFrequency (%)
tx 1296
 
7.1%
ny 1190
 
6.6%
pa 1107
 
6.1%
ca 785
 
4.3%
mi 664
 
3.7%
oh 662
 
3.6%
il 600
 
3.3%
fl 589
 
3.2%
al 586
 
3.2%
mo 511
 
2.8%
Other values (41) 10164
56.0%
2025-06-28T21:32:09.258763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4986
13.7%
N 4019
 
11.1%
M 3101
 
8.5%
I 2599
 
7.2%
T 2155
 
5.9%
L 2045
 
5.6%
O 2012
 
5.5%
C 1949
 
5.4%
Y 1822
 
5.0%
W 1332
 
3.7%
Other values (14) 10288
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4986
13.7%
N 4019
 
11.1%
M 3101
 
8.5%
I 2599
 
7.2%
T 2155
 
5.9%
L 2045
 
5.6%
O 2012
 
5.5%
C 1949
 
5.4%
Y 1822
 
5.0%
W 1332
 
3.7%
Other values (14) 10288
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4986
13.7%
N 4019
 
11.1%
M 3101
 
8.5%
I 2599
 
7.2%
T 2155
 
5.9%
L 2045
 
5.6%
O 2012
 
5.5%
C 1949
 
5.4%
Y 1822
 
5.0%
W 1332
 
3.7%
Other values (14) 10288
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4986
13.7%
N 4019
 
11.1%
M 3101
 
8.5%
I 2599
 
7.2%
T 2155
 
5.9%
L 2045
 
5.6%
O 2012
 
5.5%
C 1949
 
5.4%
Y 1822
 
5.0%
W 1332
 
3.7%
Other values (14) 10288
28.3%

zip
Real number (ℝ)

Distinct912
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48664.70299
Minimum1257
Maximum99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:09.323173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7060
Q125526
median48088
Q372042
95-th percentile94971
Maximum99783
Range98526
Interquartile range (IQR)46516

Descriptive statistics

Standard deviation27006.09701
Coefficient of variation (CV)0.5549421933
Kurtosis-1.094147485
Mean48664.70299
Median Absolute Deviation (MAD)23161
Skewness0.0844970445
Sum883459018
Variance729329275.8
MonotonicityNot monotonic
2025-06-28T21:32:09.382420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48088 58
 
0.3%
15484 58
 
0.3%
46254 58
 
0.3%
82514 56
 
0.3%
92585 53
 
0.3%
28405 52
 
0.3%
59448 51
 
0.3%
29819 50
 
0.3%
73754 50
 
0.3%
36749 49
 
0.3%
Other values (902) 17619
97.1%
ValueCountFrequency (%)
1257 26
0.1%
1330 17
0.1%
1535 9
 
< 0.1%
1545 11
0.1%
1612 6
 
< 0.1%
ValueCountFrequency (%)
99783 24
0.1%
99747 1
 
< 0.1%
99746 4
 
< 0.1%
99323 35
0.2%
99160 47
0.3%

lat
Real number (ℝ)

Distinct910
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.6054366
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:09.436978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8826
Q134.6902
median39.4717
Q342.074
95-th percentile46.0062
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.3838

Descriptive statistics

Standard deviation5.095153954
Coefficient of variation (CV)0.1319802184
Kurtosis0.8168407106
Mean38.6054366
Median Absolute Deviation (MAD)3.3929
Skewness-0.2157121257
Sum700843.096
Variance25.96059382
MonotonicityNot monotonic
2025-06-28T21:32:09.494084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.8936 58
 
0.3%
39.849 58
 
0.3%
42.5164 58
 
0.3%
43.0048 56
 
0.3%
33.7467 53
 
0.3%
34.2651 52
 
0.3%
48.2777 51
 
0.3%
36.385 50
 
0.3%
34.0326 50
 
0.3%
32.5104 49
 
0.3%
Other values (900) 17619
97.1%
ValueCountFrequency (%)
20.0271 26
0.1%
20.0827 19
0.1%
24.6557 39
0.2%
26.1184 46
0.3%
26.3304 9
 
< 0.1%
ValueCountFrequency (%)
66.6933 1
 
< 0.1%
65.6899 4
 
< 0.1%
64.7556 24
0.1%
48.8878 47
0.3%
48.8856 32
0.2%

long
Real number (ℝ)

Distinct911
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.18901247
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative18154
Negative (%)100.0%
Memory size142.0 KiB
2025-06-28T21:32:09.547005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.7957
Q1-96.798
median-87.3667
Q3-80.1248
95-th percentile-73.3113
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.6732

Descriptive statistics

Standard deviation13.87788997
Coefficient of variation (CV)-0.1538756174
Kurtosis1.988324085
Mean-90.18901247
Median Absolute Deviation (MAD)8.1772
Skewness-1.179633642
Sum-1637291.332
Variance192.5958301
MonotonicityNot monotonic
2025-06-28T21:32:09.609659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-82.9832 58
 
0.3%
-86.272 58
 
0.3%
-79.7856 58
 
0.3%
-108.8964 56
 
0.3%
-117.1721 53
 
0.3%
-77.867 52
 
0.3%
-112.8456 51
 
0.3%
-82.2027 50
 
0.3%
-98.0727 50
 
0.3%
-86.8138 49
 
0.3%
Other values (901) 17619
97.1%
ValueCountFrequency (%)
-165.6723 24
0.1%
-156.292 4
 
< 0.1%
-155.488 19
0.1%
-155.3697 26
0.1%
-153.994 1
 
< 0.1%
ValueCountFrequency (%)
-67.9503 25
0.1%
-68.5565 16
0.1%
-69.2675 8
 
< 0.1%
-69.4828 27
0.1%
-69.9576 13
0.1%

city_pop
Real number (ℝ)

Distinct835
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89124.00369
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:09.663855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile137
Q1759
median2471
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19569

Descriptive statistics

Standard deviation304167.3087
Coefficient of variation (CV)3.41285508
Kurtosis37.91932281
Mean89124.00369
Median Absolute Deviation (MAD)2223
Skewness5.613986466
Sum1617957163
Variance9.25177517 × 1010
MonotonicityNot monotonic
2025-06-28T21:32:09.722919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1312922 81
 
0.4%
1766 71
 
0.4%
910148 71
 
0.4%
1595797 70
 
0.4%
1126 70
 
0.4%
606 69
 
0.4%
2906700 64
 
0.4%
2135 63
 
0.3%
241 60
 
0.3%
237282 60
 
0.3%
Other values (825) 17475
96.3%
ValueCountFrequency (%)
23 24
0.1%
37 13
 
0.1%
43 40
0.2%
46 35
0.2%
47 9
 
< 0.1%
ValueCountFrequency (%)
2906700 64
0.4%
2504700 28
 
0.2%
2383912 4
 
< 0.1%
1595797 70
0.4%
1577385 27
 
0.1%

job
Text

Distinct479
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:09.862519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.24551063
Min length3

Characters and Unicode

Total characters367537
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowAgricultural consultant
2nd rowMuseum/gallery conservator
3rd rowPhysiotherapist
4th rowLecturer, further education
5th rowPhysiotherapist
ValueCountFrequency (%)
engineer 1823
 
4.5%
officer 1543
 
3.8%
manager 828
 
2.1%
scientist 791
 
2.0%
designer 727
 
1.8%
surveyor 643
 
1.6%
teacher 538
 
1.3%
psychologist 486
 
1.2%
research 440
 
1.1%
editor 389
 
1.0%
Other values (450) 32080
79.6%
2025-06-28T21:32:10.065438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 39289
 
10.7%
i 33387
 
9.1%
r 30751
 
8.4%
a 25636
 
7.0%
t 24907
 
6.8%
n 24727
 
6.7%
22134
 
6.0%
o 20816
 
5.7%
s 20361
 
5.5%
c 18554
 
5.0%
Other values (43) 106975
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 367537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 39289
 
10.7%
i 33387
 
9.1%
r 30751
 
8.4%
a 25636
 
7.0%
t 24907
 
6.8%
n 24727
 
6.7%
22134
 
6.0%
o 20816
 
5.7%
s 20361
 
5.5%
c 18554
 
5.0%
Other values (43) 106975
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 367537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 39289
 
10.7%
i 33387
 
9.1%
r 30751
 
8.4%
a 25636
 
7.0%
t 24907
 
6.8%
n 24727
 
6.7%
22134
 
6.0%
o 20816
 
5.7%
s 20361
 
5.5%
c 18554
 
5.0%
Other values (43) 106975
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 367537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 39289
 
10.7%
i 33387
 
9.1%
r 30751
 
8.4%
a 25636
 
7.0%
t 24907
 
6.8%
n 24727
 
6.7%
22134
 
6.0%
o 20816
 
5.7%
s 20361
 
5.5%
c 18554
 
5.0%
Other values (43) 106975
29.1%

trans_num
Text

Unique 

Distinct18154
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:10.205835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters580928
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18154 ?
Unique (%)100.0%

Sample

1st row132b52994cd55a14637b101713c4a542
2nd row74d5424269dc6b23127cefa42958cc73
3rd row658ec4d0049e69d566b8ff947d650200
4th rowc0dd77c9e62e623cd78e3df1c474184b
5th rowbd4dd5b80243e0c2da79f91e42d317ce
ValueCountFrequency (%)
132b52994cd55a14637b101713c4a542 1
 
< 0.1%
9441b1400f235c258253ce37e4b85b2f 1
 
< 0.1%
6ba3376d32486f211c70e4d86276dfcb 1
 
< 0.1%
e272af72ce38c17659ef9ce13a565271 1
 
< 0.1%
b6239805c0b622dc3a34fcc6ae978609 1
 
< 0.1%
7181d8ca036c004bc5f2362d41d58e90 1
 
< 0.1%
8bafee6eea25e6774d2fa345d7ed37d7 1
 
< 0.1%
2248d41b59c61fdcb216cfe898dd3c2b 1
 
< 0.1%
70398bd37494f96e5ed646aefaed1bda 1
 
< 0.1%
bd4dd5b80243e0c2da79f91e42d317ce 1
 
< 0.1%
Other values (18144) 18144
99.9%
2025-06-28T21:32:10.404624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 36503
 
6.3%
6 36487
 
6.3%
0 36484
 
6.3%
f 36437
 
6.3%
5 36430
 
6.3%
8 36413
 
6.3%
c 36366
 
6.3%
1 36334
 
6.3%
3 36321
 
6.3%
7 36307
 
6.2%
Other values (6) 216846
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 580928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 36503
 
6.3%
6 36487
 
6.3%
0 36484
 
6.3%
f 36437
 
6.3%
5 36430
 
6.3%
8 36413
 
6.3%
c 36366
 
6.3%
1 36334
 
6.3%
3 36321
 
6.3%
7 36307
 
6.2%
Other values (6) 216846
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 580928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 36503
 
6.3%
6 36487
 
6.3%
0 36484
 
6.3%
f 36437
 
6.3%
5 36430
 
6.3%
8 36413
 
6.3%
c 36366
 
6.3%
1 36334
 
6.3%
3 36321
 
6.3%
7 36307
 
6.2%
Other values (6) 216846
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 580928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 36503
 
6.3%
6 36487
 
6.3%
0 36484
 
6.3%
f 36437
 
6.3%
5 36430
 
6.3%
8 36413
 
6.3%
c 36366
 
6.3%
1 36334
 
6.3%
3 36321
 
6.3%
7 36307
 
6.2%
Other values (6) 216846
37.3%

merch_lat
Real number (ℝ)

Distinct18142
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.60902014
Minimum19.063792
Maximum66.145366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:10.465054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.063792
5-th percentile29.790159
Q134.7687095
median39.4580645
Q342.04184125
95-th percentile46.12884965
Maximum66.145366
Range47.081574
Interquartile range (IQR)7.27313175

Descriptive statistics

Standard deviation5.128599765
Coefficient of variation (CV)0.1328342379
Kurtosis0.8276399031
Mean38.60902014
Median Absolute Deviation (MAD)3.3929395
Skewness-0.2063281166
Sum700908.1517
Variance26.30253555
MonotonicityNot monotonic
2025-06-28T21:32:10.530100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.680376 2
 
< 0.1%
41.000857 2
 
< 0.1%
43.790803 2
 
< 0.1%
30.867051 2
 
< 0.1%
38.927328 2
 
< 0.1%
39.415758 2
 
< 0.1%
39.74328 2
 
< 0.1%
34.079644 2
 
< 0.1%
40.773936 2
 
< 0.1%
39.667428 2
 
< 0.1%
Other values (18132) 18134
99.9%
ValueCountFrequency (%)
19.063792 1
< 0.1%
19.104759 1
< 0.1%
19.205749 1
< 0.1%
19.209212 1
< 0.1%
19.217276 1
< 0.1%
ValueCountFrequency (%)
66.145366 1
< 0.1%
65.931498 1
< 0.1%
65.798 1
< 0.1%
65.726855 1
< 0.1%
65.687491 1
< 0.1%

merch_long
Real number (ℝ)

Distinct18148
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.18463133
Minimum-166.661968
Maximum-67.025106
Zeros0
Zeros (%)0.0%
Negative18154
Negative (%)100.0%
Memory size142.0 KiB
2025-06-28T21:32:10.588427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.661968
5-th percentile-119.4427951
Q1-96.82199175
median-87.315356
Q3-80.16166975
95-th percentile-73.27150515
Maximum-67.025106
Range99.636862
Interquartile range (IQR)16.660322

Descriptive statistics

Standard deviation13.89290252
Coefficient of variation (CV)-0.1540495572
Kurtosis1.976676901
Mean-90.18463133
Median Absolute Deviation (MAD)8.289568
Skewness-1.175829509
Sum-1637211.797
Variance193.0127404
MonotonicityNot monotonic
2025-06-28T21:32:10.644918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-96.976292 2
 
< 0.1%
-82.076206 2
 
< 0.1%
-80.156771 2
 
< 0.1%
-83.498739 2
 
< 0.1%
-100.763163 2
 
< 0.1%
-96.477598 2
 
< 0.1%
-105.917078 1
 
< 0.1%
-122.526463 1
 
< 0.1%
-91.806102 1
 
< 0.1%
-117.923376 1
 
< 0.1%
Other values (18138) 18138
99.9%
ValueCountFrequency (%)
-166.661968 1
< 0.1%
-166.524198 1
< 0.1%
-166.508364 1
< 0.1%
-166.444781 1
< 0.1%
-166.265842 1
< 0.1%
ValueCountFrequency (%)
-67.025106 1
< 0.1%
-67.066178 1
< 0.1%
-67.084269 1
< 0.1%
-67.191409 1
< 0.1%
-67.260368 1
< 0.1%

is_fraud
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005783849289
Minimum0
Maximum1
Zeros18049
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:10.686359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07583345667
Coefficient of variation (CV)13.11124355
Kurtosis167.9476389
Mean0.005783849289
Median Absolute Deviation (MAD)0
Skewness13.03568703
Sum105
Variance0.005750713151
MonotonicityNot monotonic
2025-06-28T21:32:10.721237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 18049
99.4%
1 105
 
0.6%
ValueCountFrequency (%)
0 18049
99.4%
1 105
 
0.6%
ValueCountFrequency (%)
1 105
 
0.6%
0 18049
99.4%

merch_zipcode
Real number (ℝ)

Missing 

Distinct10587
Distinct (%)68.8%
Missing2758
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean46645.1642
Minimum1007
Maximum99402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:10.775905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1007
5-th percentile6905.25
Q124597
median45843.5
Q368034
95-th percentile92914.75
Maximum99402
Range98395
Interquartile range (IQR)43437

Descriptive statistics

Standard deviation25931.15826
Coefficient of variation (CV)0.5559238285
Kurtosis-0.9928220392
Mean46645.1642
Median Absolute Deviation (MAD)21607.5
Skewness0.1537433659
Sum718148948
Variance672424968.9
MonotonicityNot monotonic
2025-06-28T21:32:10.847584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16374 8
 
< 0.1%
35010 7
 
< 0.1%
29015 7
 
< 0.1%
47978 7
 
< 0.1%
36272 7
 
< 0.1%
21661 7
 
< 0.1%
76424 7
 
< 0.1%
47448 7
 
< 0.1%
39051 7
 
< 0.1%
82501 6
 
< 0.1%
Other values (10577) 15326
84.4%
(Missing) 2758
 
15.2%
ValueCountFrequency (%)
1007 4
< 0.1%
1013 1
 
< 0.1%
1022 1
 
< 0.1%
1028 1
 
< 0.1%
1034 1
 
< 0.1%
ValueCountFrequency (%)
99402 1
< 0.1%
99401 1
< 0.1%
99362 1
< 0.1%
99361 1
< 0.1%
99360 1
< 0.1%
Distinct18150
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:11.027321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters344926
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18146 ?
Unique (%)> 99.9%

Sample

1st row2020-06-20 19:28:17
2nd row2019-03-10 13:36:01
3rd row2020-05-11 07:05:43
4th row2019-05-18 08:58:03
5th row2019-09-07 22:59:24
ValueCountFrequency (%)
2019-12-01 102
 
0.3%
2019-12-08 102
 
0.3%
2019-12-30 100
 
0.3%
2019-12-28 93
 
0.3%
2019-12-29 88
 
0.2%
2019-12-09 86
 
0.2%
2019-12-02 85
 
0.2%
2019-12-16 84
 
0.2%
2019-12-22 83
 
0.2%
2019-12-21 76
 
0.2%
Other values (16841) 35409
97.5%
2025-06-28T21:32:11.360619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 63404
18.4%
2 49842
14.5%
1 47977
13.9%
- 36308
10.5%
: 36308
10.5%
9 20866
 
6.0%
18154
 
5.3%
3 16615
 
4.8%
5 15124
 
4.4%
4 14961
 
4.3%
Other values (3) 25367
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 344926
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 63404
18.4%
2 49842
14.5%
1 47977
13.9%
- 36308
10.5%
: 36308
10.5%
9 20866
 
6.0%
18154
 
5.3%
3 16615
 
4.8%
5 15124
 
4.4%
4 14961
 
4.3%
Other values (3) 25367
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 344926
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 63404
18.4%
2 49842
14.5%
1 47977
13.9%
- 36308
10.5%
: 36308
10.5%
9 20866
 
6.0%
18154
 
5.3%
3 16615
 
4.8%
5 15124
 
4.4%
4 14961
 
4.3%
Other values (3) 25367
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 344926
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 63404
18.4%
2 49842
14.5%
1 47977
13.9%
- 36308
10.5%
: 36308
10.5%
9 20866
 
6.0%
18154
 
5.3%
3 16615
 
4.8%
5 15124
 
4.4%
4 14961
 
4.3%
Other values (3) 25367
7.4%
Distinct923
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:11.596029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters1161856
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st row441a0d1e57a79a81b980fb65cab3a8a9f00e3247f5e9ccbd899bcfe02de8c8f7
2nd rowf4ba62b68cb080e98a3e8776630bdf85708b21d8ee2eb6bfbba113916719040e
3rd row0700ec170ff470df122b6fd9c571b87200d4258058507a29ac2301681ab7f3d8
4th row00e2ad8fed12b1ef527b71aa5edf6eb494901210121c09b349c1573b7ea4d177
5th rowa71ab44d5734c7d21b589c2d501a78f99fa306dae605daa4b5679085ab12f592
ValueCountFrequency (%)
2776266470b9b04633f76717e941c373187376e99ad5b801aa15f2100ea037a3 58
 
0.3%
37ea83ebd66889dccf50aece485b02908680862e4950426a66b08676b789219c 58
 
0.3%
f1a9eb73c5421df85abb7d2a9429788273afa28826a47fc2140ca0d2014533a4 53
 
0.3%
a3a62304e05705d4a85684b3b027dd7e30904d784d14e48dafbcf386705ebf56 52
 
0.3%
a51fcb4f97c466a4aa8faabeebf0c91a6d5b20d8162ddb8cd430c49dc3ba6c87 51
 
0.3%
adcf6522b3114d26427d7aa4f595be549bc8d9531aa1d4daf470d577b45f4d43 50
 
0.3%
3ca14369f59c56e79d6bad9db761ad082be4dcb4a1935edbc04fe81c3a1487bf 49
 
0.3%
e8ea2c5345495b65ee7b4863eaa23dbb32b202d8135cd5e41e292d2c7441add6 48
 
0.3%
931516cac2466fea37966c567868dcfe8dc94dfd379001a8d82a7e966205b6de 48
 
0.3%
5b9e1e38487f8570c6ec1f33df8fe20aef8ec38a7a3329edd84614fec3a39be3 48
 
0.3%
Other values (913) 17639
97.2%
2025-06-28T21:32:11.847186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 74836
 
6.4%
e 74206
 
6.4%
2 74163
 
6.4%
3 74014
 
6.4%
7 73903
 
6.4%
1 73833
 
6.4%
5 73072
 
6.3%
d 72977
 
6.3%
f 72521
 
6.2%
4 72396
 
6.2%
Other values (6) 425935
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1161856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74836
 
6.4%
e 74206
 
6.4%
2 74163
 
6.4%
3 74014
 
6.4%
7 73903
 
6.4%
1 73833
 
6.4%
5 73072
 
6.3%
d 72977
 
6.3%
f 72521
 
6.2%
4 72396
 
6.2%
Other values (6) 425935
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1161856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74836
 
6.4%
e 74206
 
6.4%
2 74163
 
6.4%
3 74014
 
6.4%
7 73903
 
6.4%
1 73833
 
6.4%
5 73072
 
6.3%
d 72977
 
6.3%
f 72521
 
6.2%
4 72396
 
6.2%
Other values (6) 425935
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1161856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74836
 
6.4%
e 74206
 
6.4%
2 74163
 
6.4%
3 74014
 
6.4%
7 73903
 
6.4%
1 73833
 
6.4%
5 73072
 
6.3%
d 72977
 
6.3%
f 72521
 
6.2%
4 72396
 
6.2%
Other values (6) 425935
36.7%
Distinct339
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:12.039725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters1161856
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8d289ec537b6e2edd43e9f464fcbf2b42c1f34fcaf0aea680d411cb4656a3d97
2nd row88e27fb4b1703bb2671edab87148ee492d8294d99a32895d39ec79fb27967480
3rd rowcccdf4e07a1abca8eb65d691c01ee281f75ddb8c4683a7064a2d9322865e03a0
4th rowc7a11e9a55de2b146e3b2d75e9112ee6af4e5b439658e725e6afca92e93bf0bd
5th row864282b76c39e6748fa8b9accb2953bc89a3ec4f6c5ca1627624d6a58edd5619
ValueCountFrequency (%)
f3b46c145f69a06b8071dc1339081ce4923ee5f5ef55af2e5749ee1380c007ae 389
 
2.1%
fb338e53da57ec89c638f24e8f87697a70d0c0d8e4cc3a56f754211d2c6f0444 305
 
1.7%
2238dd61a1bf83816b40ad894518814b8edf7221d84d897ffd2c0466ace07c41 290
 
1.6%
f089eaef57aba315bc0e1455985c0c8e40c247f073ce1f4c5a1f8ffde8773176 288
 
1.6%
a6b54c20a7b96eeac1a911e6da3124a560fe6dc042ebf270e3676e7095b95652 277
 
1.5%
bda0a01c76aae97ef15d6b792f3dc3cbb6609d611e9c563a54a1393fe685e31b 253
 
1.4%
a8cfcd74832004951b4408cdb0a5dbcd8c7e52d43f7fe244bf720582e05241da 251
 
1.4%
aebac53c46bbeff10fdd26ca0e2196a9bfc1d19bf88eb1efd65a36151c581051 224
 
1.2%
9345a35a6fdf174dff7219282a3ae4879790dbb785c70f6fff91e32fafd66eab 222
 
1.2%
ab81b2d493f645aad3fe13342c08fd450b92f1f1bf9a8c27b62fc4c4054bdd51 216
 
1.2%
Other values (329) 15439
85.0%
2025-06-28T21:32:12.289845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 76859
 
6.6%
c 76745
 
6.6%
e 74752
 
6.4%
4 74376
 
6.4%
d 74236
 
6.4%
3 74115
 
6.4%
2 73287
 
6.3%
f 72649
 
6.3%
b 72641
 
6.3%
5 72331
 
6.2%
Other values (6) 419865
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1161856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 76859
 
6.6%
c 76745
 
6.6%
e 74752
 
6.4%
4 74376
 
6.4%
d 74236
 
6.4%
3 74115
 
6.4%
2 73287
 
6.3%
f 72649
 
6.3%
b 72641
 
6.3%
5 72331
 
6.2%
Other values (6) 419865
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1161856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 76859
 
6.6%
c 76745
 
6.6%
e 74752
 
6.4%
4 74376
 
6.4%
d 74236
 
6.4%
3 74115
 
6.4%
2 73287
 
6.3%
f 72649
 
6.3%
b 72641
 
6.3%
5 72331
 
6.2%
Other values (6) 419865
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1161856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 76859
 
6.6%
c 76745
 
6.6%
e 74752
 
6.4%
4 74376
 
6.4%
d 74236
 
6.4%
3 74115
 
6.4%
2 73287
 
6.3%
f 72649
 
6.3%
b 72641
 
6.3%
5 72331
 
6.2%
Other values (6) 419865
36.1%
Distinct468
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:12.489375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters1161856
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowd826fb138cc2d88f7805eecf9e34499e71308ade6d858fbec35d5646703af9a3
2nd row3495e757855a5c678addcf32516274e2962d0572f065378dba689e22168f28dd
3rd row683313d69084379711e47f9e23cc8d7a4cf1a3fac5b48a8b6c560232ef7619a6
4th rowcec82e6bc635a807a12669feb8e8288194087381cd5f897371aebb74be209396
5th row0ceec16c3d3170168bd3acfd48748d95eb98992c15399cd9a495ec2212f71bc2
ValueCountFrequency (%)
9f542590100424c92a6ae40860f7017ac5dfbcff3cb49b36eace29b068e0d8e1 379
 
2.1%
6e8d8801f51aafe8426f8d95c75de8f3f404ccae62f116070ca643403484a243 287
 
1.6%
3013b18f4387bbe12cdb6d3ba9aa45a36adce32485da62113f97163f16beda66 285
 
1.6%
683313d69084379711e47f9e23cc8d7a4cf1a3fac5b48a8b6c560232ef7619a6 280
 
1.5%
503046646e0c61a5fe27c74a0da0aea7affe2c6a6cc1c77d42241db9e77d0716 249
 
1.4%
fe16a94194b3857b569579be460d2079ebcb36e9b8b03649191a92e8b0fe35c9 224
 
1.2%
1cd8e5f4826e1e694b14ca74ea2f39975dbdfc9ab93b47a7ae7889144c5bc36b 208
 
1.1%
11a4f480c4c0a039dd074473f0ffbb5de073500b3c25d08a774143b1ee4a0c9b 190
 
1.0%
c625ca57418821d8e717df1b71bf589a042d8fc0f0a2c3776090e155d2d377d3 187
 
1.0%
29940161fe9fe57993cb27e0a07668bd018b43d4afae3e13083ccf169e2bc106 166
 
0.9%
Other values (458) 15699
86.5%
2025-06-28T21:32:12.747115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 76776
 
6.6%
1 76177
 
6.6%
c 76109
 
6.6%
4 75025
 
6.5%
a 74814
 
6.4%
9 73765
 
6.3%
7 71992
 
6.2%
d 71878
 
6.2%
0 71875
 
6.2%
f 71788
 
6.2%
Other values (6) 421657
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1161856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 76776
 
6.6%
1 76177
 
6.6%
c 76109
 
6.6%
4 75025
 
6.5%
a 74814
 
6.4%
9 73765
 
6.3%
7 71992
 
6.2%
d 71878
 
6.2%
0 71875
 
6.2%
f 71788
 
6.2%
Other values (6) 421657
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1161856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 76776
 
6.6%
1 76177
 
6.6%
c 76109
 
6.6%
4 75025
 
6.5%
a 74814
 
6.4%
9 73765
 
6.3%
7 71992
 
6.2%
d 71878
 
6.2%
0 71875
 
6.2%
f 71788
 
6.2%
Other values (6) 421657
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1161856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 76776
 
6.6%
1 76177
 
6.6%
c 76109
 
6.6%
4 75025
 
6.5%
a 74814
 
6.4%
9 73765
 
6.3%
7 71992
 
6.2%
d 71878
 
6.2%
0 71875
 
6.2%
f 71788
 
6.2%
Other values (6) 421657
36.3%

age
Real number (ℝ)

Distinct82
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.74143439
Minimum14
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.0 KiB
2025-06-28T21:32:12.804022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile21
Q133
median44
Q357
95-th percentile79
Maximum95
Range81
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.29029956
Coefficient of variation (CV)0.3780008168
Kurtosis-0.193151585
Mean45.74143439
Median Absolute Deviation (MAD)12
Skewness0.5855991687
Sum830390
Variance298.9544589
MonotonicityNot monotonic
2025-06-28T21:32:12.862387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 595
 
3.3%
35 535
 
2.9%
34 518
 
2.9%
43 512
 
2.8%
46 499
 
2.7%
44 485
 
2.7%
32 484
 
2.7%
33 467
 
2.6%
31 428
 
2.4%
42 423
 
2.3%
Other values (72) 13208
72.8%
ValueCountFrequency (%)
14 51
0.3%
15 91
0.5%
16 42
0.2%
17 26
 
0.1%
18 92
0.5%
ValueCountFrequency (%)
95 3
 
< 0.1%
94 8
 
< 0.1%
93 51
0.3%
92 61
0.3%
91 67
0.4%